Analyze Associations between Sleep and Caregiving

Load data

##Libraries
library(tidyverse)

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✔ tibble  1.4.2          ✔ dplyr   0.7.5     
✔ tidyr   0.8.1          ✔ stringr 1.3.1     
✔ readr   1.1.1          ✔ forcats 0.3.0     
── Conflicts ──────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::expand() masks Matrix::expand()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(lme4)
library(lmerTest)
library(jtools)

The most recent jtools update (to 1.0.0) was a major update. Please check out
http://www.jtools.jacob-long.com/news/ for details on what is new. This message will go away in 3
days.

library(lcmm)

Loading required package: survival

Attaching package: ‘lcmm’

The following objects are masked from ‘package:lme4’:

    fixef, ranef

##Parameters
intervention_file <- "~/Desktop/BABIES/manber_sleep/ID and Tx Arm.csv"
plot_trajectories_id <- function(dataframe, x, y) {
  x_variable = enquo(x)
  y_variable = enquo(y)
  dataframe %>% 
  ggplot(aes(!! x_variable, !! y_variable)) +
    geom_jitter() +
    geom_smooth(
      aes(color = as.factor(ID)),
      method = "lm", 
      se = FALSE,
      alpha = 1/2
    ) +
    geom_smooth(
      method = "lm", 
      se = FALSE, 
      color = "black",
      size = 2
    ) +
    theme_minimal() +
    theme(
      legend.position = "none"
    )
}
plot_trajectories <- function(dataframe, x, y, color, method) {
  x_variable = enquo(x)
  y_variable = enquo(y)
  color_variable = enquo(color)
  
  dataframe %>% 
  ggplot(aes(x = !! x_variable, y = !! y_variable, color = !! color_variable)) +
    geom_jitter() +
    geom_smooth(
      method = method, 
      se = FALSE, 
      size = 2
    ) +
    theme_minimal() 
}

data_wf <-
  free_play_wf %>% 
  left_join(free_play_fa %>% select(ID, Factor1:Factor3), by = "ID") %>%  
  left_join(read_csv(intervention_file), by = "ID") %>% 
  left_join(questionnaires, by = "ID") %>% 
  left_join(maternal_actigraphy_summary, by = "ID") 

Parsed with column specification:
cols(
  ID = col_integer(),
  Condition = col_character()
)

data_lf <-
  free_play_lf %>% 
  filter(Episode != 6) %>% 
  left_join(read_csv(intervention_file), by = "ID") %>% 
  left_join(questionnaires, by = "ID") %>% 
  left_join(maternal_actigraphy_summary, by = "ID") %>% 
  mutate(
    episode_re = as.integer(
      recode(
        Episode,
        "1" = "0",
        "2" = "1",
        "3" = "2",
        "4" = "3",
        "5" = "4"
      )
    ),
    wake_time_grp = if_else(
      mean_wake_time > 60,
      "higher", "lower"
    ),
    efficiency_grp = if_else(
      mean_efficiency > median(mean_efficiency, na.rm = TRUE),
      "higher", "lower"
    ),
    ISI_grp = if_else(
      ISI_total > median(ISI_total, na.rm = TRUE),
      "higher", "lower"
    ),
    ISIP_grp = if_else(
      ISI_total > median(ISIP_total, na.rm = TRUE),
      "higher", "lower"
    )
  )

Parsed with column specification:
cols(
  ID = col_integer(),
  Condition = col_character()
)

  
glimpse(data_wf)

Observations: 67
Variables: 127
$ ID                                   <dbl> 11002, 11004, 11016, 11017, 11021, 11022, 11023, 11025, ...
$ `1_detach_fin`                       <dbl> 1.0, 1.0, 1.0, 1.0, 1.0, 1.5, 3.5, 1.0, 1.0, 1.0, 1.5, 1...
$ `1_intrus_fin`                       <dbl> 3.5, 2.0, 5.0, 6.0, 2.0, 2.0, 3.0, 4.5, 4.0, 2.0, 2.0, 3...
$ `1_negreg_fin`                       <dbl> 1.0, 1.0, 1.5, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0, 1.0, 1.0, 1...
$ `1_posreg_fin`                       <dbl> 5.5, 6.0, 5.0, 6.0, 3.0, 6.0, 4.0, 5.5, 6.0, 5.0, 5.5, 4...
$ `1_sens_fin`                         <dbl> 4.0, 4.5, 3.5, 2.5, 4.0, 5.0, 3.0, 3.5, 3.5, 5.0, 4.0, 4...
$ `1_stim_fin`                         <dbl> 3.5, 3.0, 3.0, 3.0, 2.0, 3.5, 2.0, NA, 3.5, 2.5, 2.0, 5....
$ `2_detach_fin`                       <dbl> 1.0, 1.0, 1.5, 1.0, 1.0, 1.0, 3.0, 1.5, 1.0, 1.0, 1.0, 1...
$ `2_intrus_fin`                       <dbl> 5.0, 2.5, 5.5, 5.5, 1.5, 2.5, 4.5, 3.5, 5.0, 2.0, 2.5, 3...
$ `2_negreg_fin`                       <dbl> 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.5, 1.0, 1.0, 2...
$ `2_posreg_fin`                       <dbl> 6.0, 6.0, 4.5, 6.0, 3.5, 5.5, 4.0, 5.0, 5.0, 6.0, 6.0, 4...
$ `2_sens_fin`                         <dbl> 3.5, 5.0, 3.0, 3.0, 4.0, 5.0, 3.0, 3.0, 3.0, 6.0, 4.0, 4...
$ `2_stim_fin`                         <dbl> 4.0, 4.5, 2.0, 3.5, 2.5, 3.5, 3.0, NA, 3.0, 4.0, 3.0, 5....
$ `3_detach_fin`                       <dbl> 1.0, 1.5, 1.0, 1.0, 1.0, 1.0, 2.5, 1.0, 1.0, 1.0, 1.0, 1...
$ `3_intrus_fin`                       <dbl> 6.0, 6.0, 4.5, 5.5, 1.0, 2.5, 4.5, 3.0, 5.0, 1.0, 1.5, 2...
$ `3_negreg_fin`                       <dbl> 1.5, 1.0, 1.5, 1.5, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 1...
$ `3_posreg_fin`                       <dbl> 5.0, 6.0, 5.0, 5.0, 3.5, 5.5, 5.0, 5.5, 5.5, 5.0, 6.0, 4...
$ `3_sens_fin`                         <dbl> 2.5, 3.0, 3.5, 2.5, 4.5, 4.5, 3.5, 4.5, 3.0, 5.5, 5.0, 3...
$ `3_stim_fin`                         <dbl> 3.5, 2.0, 3.0, 3.0, 2.0, 3.0, 2.5, NA, 2.5, 3.5, 3.5, 4....
$ `4_detach_fin`                       <dbl> 1.5, 1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0, 1.5, 1.0, 1.0, 1...
$ `4_intrus_fin`                       <dbl> 6.5, 5.5, 3.5, 5.0, 1.5, 4.0, 5.0, 4.5, 5.5, 1.0, 3.5, 3...
$ `4_negreg_fin`                       <dbl> 2.0, 1.5, 1.5, 1.0, 2.0, 2.5, 3.5, 1.0, 3.0, 1.0, 2.0, 2...
$ `4_posreg_fin`                       <dbl> 3.5, 5.5, 4.5, 6.0, 3.0, 4.0, 3.5, 5.5, 4.5, 6.0, 5.0, 4...
$ `4_sens_fin`                         <dbl> 2.0, 3.5, 4.0, 3.5, 4.0, 3.0, 2.5, 3.5, 2.5, 6.0, 3.5, 3...
$ `4_stim_fin`                         <dbl> 3.0, 3.0, 3.0, 4.0, 2.5, 2.0, 2.5, NA, 2.0, 4.5, 2.5, 3....
$ `5_detach_fin`                       <dbl> 1.0, 1.0, NA, 1.0, 1.0, 2.0, 1.0, 1.0, 1.5, 1.0, 3.0, 1....
$ `5_intrus_fin`                       <dbl> 4.5, 6.0, NA, 5.5, 3.0, 2.5, 5.5, 5.0, 5.5, 1.0, 2.0, 3....
$ `5_negreg_fin`                       <dbl> 1.0, 1.5, NA, 2.0, 1.0, 1.0, 1.5, 2.5, 3.5, 1.0, 2.0, 1....
$ `5_posreg_fin`                       <dbl> 5.0, 5.0, NA, 4.5, 3.5, 4.5, 4.5, 4.0, 4.0, 5.0, 4.0, 4....
$ `5_sens_fin`                         <dbl> 3.5, 3.0, NA, 3.0, 3.0, 4.0, 3.0, 3.0, 2.5, 6.0, 2.5, 4....
$ `5_stim_fin`                         <dbl> 3.5, 2.0, NA, 2.5, 2.0, 2.0, 2.5, NA, 2.0, 4.0, 2.0, 3.0...
$ `6_detach_fin`                       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
$ `6_intrus_fin`                       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
$ `6_negreg_fin`                       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
$ `6_posreg_fin`                       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
$ `6_sens_fin`                         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
$ `6_stim_fin`                         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
$ sens_M                               <dbl> 3.10, 3.80, 3.50, 2.90, 3.90, 4.30, 3.00, 3.50, 2.90, 5....
$ intrus_M                             <dbl> 5.100, 4.400, 4.625, 5.500, 1.800, 2.700, 4.500, 4.100, ...
$ posreg_M                             <dbl> 5.000, 5.700, 4.750, 5.500, 3.300, 5.100, 4.200, 5.100, ...
$ stim_M                               <dbl> 3.50, 2.90, 2.75, 3.20, 2.20, 2.80, 2.50, NaN, 2.60, 3.7...
$ detach_M                             <dbl> 1.100, 1.100, 1.125, 1.000, 1.000, 1.400, 2.200, 1.100, ...
$ negreg_M                             <dbl> 1.300, 1.200, 1.625, 1.300, 1.200, 1.300, 2.100, 1.300, ...
$ Factor1                              <dbl> 0.41750725, 0.44482017, 0.22825145, 0.42940655, -0.99117...
$ Factor2                              <dbl> 1.120033983, 0.582266698, 0.666892952, 1.544157232, -1.2...
$ Factor3                              <dbl> -0.555330932, 0.492687275, -0.878599503, 0.209182110, -2...
$ Condition                            <chr> "ABTI", "CBTI", "ABTI", "CBTI", "ABTI", "ABTI", "ABTI", ...
$ ISI_total                            <int> 15, 9, 11, 1, 0, 5, 2, 4, 4, 2, 5, 2, 10, 8, 14, 0, 3, 2...
$ ISIP_total                           <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, 9, 3, 11,...
$ EPDS_total                           <int> 12, 4, 12, 4, 0, 6, 0, 1, 1, 0, 0, 1, 15, 1, 10, 3, 3, 8...
$ B_sleepnight                         <dbl> NA, 600, 600, 600, 600, 540, 480, 480, 480, 600, 540, 60...
$ B_sleepday                           <dbl> NA, 180, 270, 180, 300, 330, 360, 240, 480, 240, 180, 90...
$ B_nightwakeful                       <dbl> NA, 90, 10, 30, 5, 5, 10, 20, 150, 0, 15, 20, 20, 120, 1...
$ B_gotosleep                          <dbl> NA, 120, 0, 45, 20, 30, 20, 20, 20, 30, 30, 15, 60, 30, ...
$ analysis_name                        <chr> "BS_6.16.17", "BS_6.16.17", "11016_3_01.25.18", NA, "110...
$ data_start_date                      <chr> "11/12/13", "12/5/13", "5/29/14", NA, "7/18/14", "7/28/1...
$ data_start_time                      <time> 11:15:00, 14:09:00, 12:31:00,       NA, 09:34:00, 12:24...
$ interval_type                        <chr> "Sleep Summary", "Sleep Summary", "Sleep Summary", NA, "...
$ start_date                           <dbl> NaN, NaN, NaN, NA, NaN, NaN, NA, NaN, NaN, NaN, NaN, NaN...
$ start_time                           <dbl> NaN, NaN, NaN, NA, NaN, NaN, NA, NaN, NaN, NaN, NaN, NaN...
$ end_date                             <dbl> NaN, NaN, NaN, NA, NaN, NaN, NA, NaN, NaN, NaN, NaN, NaN...
$ end_time                             <dbl> NaN, NaN, NaN, NA, NaN, NaN, NA, NaN, NaN, NaN, NaN, NaN...
$ `Average(n)_avg_sleep_b`             <dbl> 10.75, 24.35, 19.68, NA, 35.61, 32.23, NA, 33.00, 18.12,...
$ `Average(n)_avg_wake_b`              <dbl> 3.92, 2.35, 3.26, NA, 1.79, 3.32, NA, 2.62, 2.60, 1.70, ...
$ `Average(n)_duration`                <dbl> 361.29, 512.57, 399.43, NA, 514.17, 471.50, NA, 459.86, ...
$ mean_efficiency                      <dbl> 63.08, 84.85, 69.08, NA, 93.18, 84.08, NA, 82.11, 81.67,...
$ `Average(n)_number_of_sleep_bouts`   <dbl> 25.43, 20.57, 17.71, NA, 14.67, 13.92, NA, 14.86, 25.88,...
$ `Average(n)_number_of_wake_bouts`    <dbl> 25.14, 20.29, 17.29, NA, 13.83, 13.17, NA, 14.43, 25.00,...
$ `Average(n)_onset_latency`           <dbl> 15.86, 23.43, 46.29, NA, 2.67, 16.75, NA, 26.29, 22.88, ...
$ `Average(n)_percent_invalid_sw`      <dbl> 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ `Average(n)_percent_sleep`           <dbl> 73.21, 90.20, 86.66, NA, 95.35, 90.58, NA, 89.71, 87.87,...
$ `Average(n)_percent_wake`            <dbl> 26.79, 9.80, 13.34, NA, 4.65, 9.42, NA, 10.29, 12.13, 6....
$ mean_sleep_time                      <dbl> 261.57, 462.71, 344.29, NA, 490.17, 426.42, NA, 415.29, ...
$ mean_wake_time                       <dbl> 99.71, 49.86, 55.14, NA, 24.00, 45.08, NA, 44.57, 61.88,...
$ `Average(n)_waso`                    <dbl> 99.71, 49.86, 55.14, NA, 24.00, 45.08, NA, 44.57, 61.88,...
$ `Maximum(n)_avg_sleep_b`             <dbl> 17.41, 36.36, 25.21, NA, 55.13, 46.64, NA, 52.75, 25.00,...
$ `Maximum(n)_avg_wake_b`              <dbl> 6.44, 3.46, 5.72, NA, 2.23, 8.12, NA, 6.50, 5.82, 2.11, ...
$ `Maximum(n)_duration`                <dbl> 474, 559, 582, NA, 556, 558, NA, 553, 594, 541, 691, 482...
$ `Maximum(n)_efficiency`              <dbl> 72.54, 93.76, 76.38, NA, 94.68, 92.46, NA, 92.34, 89.85,...
$ `Maximum(n)_number_of_sleep_bouts`   <dbl> 31, 28, 20, NA, 19, 18, NA, 25, 33, 20, 16, 29, 32, 28, ...
$ `Maximum(n)_number_of_wake_bouts`    <dbl> 31, 28, 21, NA, 18, 17, NA, 24, 32, 20, 15, 30, 31, 28, ...
$ `Maximum(n)_onset_latency`           <dbl> 62, 56, 167, NA, 12, 53, NA, 151, 61, 1, 21, 68, 0, 27, ...
$ `Maximum(n)_percent_invalid_sw`      <dbl> 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ `Maximum(n)_percent_sleep`           <dbl> 83.62, 95.20, 91.74, NA, 96.92, 95.69, NA, 97.24, 93.82,...
$ `Maximum(n)_percent_wake`            <dbl> 36.98, 19.48, 17.70, NA, 6.25, 27.54, NA, 39.39, 21.15, ...
$ `Maximum(n)_sleep_time`              <dbl> 300, 524, 479, NA, 534, 513, NA, 513, 537, 517, 643, 431...
$ `Maximum(n)_wake_time`               <dbl> 174, 97, 103, NA, 31, 138, NA, 156, 99, 34, 60, 157, 127...
$ `Maximum(n)_waso`                    <dbl> 174, 97, 103, NA, 31, 138, NA, 156, 99, 34, 60, 157, 127...
$ `Minimum(n)_avg_sleep_b`             <dbl> 7.84, 14.32, 15.10, NA, 24.84, 20.17, NA, 9.60, 14.29, 1...
$ `Minimum(n)_avg_wake_b`              <dbl> 2.56, 1.67, 1.67, NA, 1.28, 2.00, NA, 1.67, 1.65, 1.40, ...
$ `Minimum(n)_duration`                <dbl> 297, 449, 279, NA, 455, 378, NA, 394, 443, 328, 464, 410...
$ `Minimum(n)_efficiency`              <dbl> 50.42, 72.12, 43.17, NA, 90.29, 66.61, NA, 43.80, 70.29,...
$ `Minimum(n)_number_of_sleep_bouts`   <dbl> 17, 14, 11, NA, 8, 9, NA, 8, 17, 10, 3, 8, 32, 16, 3, 15...
$ `Minimum(n)_number_of_wake_bouts`    <dbl> 17, 14, 11, NA, 7, 9, NA, 7, 17, 9, 3, 8, 31, 15, 2, 15,...
$ `Minimum(n)_onset_latency`           <dbl> 0, 1, 0, NA, 0, 0, NA, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, ...
$ `Minimum(n)_percent_invalid_sw`      <dbl> 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ `Minimum(n)_percent_sleep`           <dbl> 63.02, 80.52, 82.30, NA, 93.75, 72.46, NA, 60.61, 78.85,...
$ `Minimum(n)_percent_wake`            <dbl> 16.38, 4.80, 8.26, NA, 3.08, 4.31, NA, 2.76, 6.18, 4.44,...
$ `Minimum(n)_sleep_time`              <dbl> 235, 401, 240, NA, 441, 349, NA, 240, 369, 307, 461, 263...
$ `Minimum(n)_wake_time`               <dbl> 58, 25, 30, NA, 14, 18, NA, 12, 28, 19, 3, 23, 127, 22, ...
$ `Minimum(n)_waso`                    <dbl> 58, 25, 30, NA, 14, 18, NA, 12, 28, 19, 3, 23, 127, 22, ...
$ n_avg_sleep_b                        <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_avg_wake_b                         <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_duration                           <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_efficiency                         <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_number_of_sleep_bouts              <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_number_of_wake_bouts               <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_onset_latency                      <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_percent_invalid_sw                 <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_percent_sleep                      <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_percent_wake                       <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_sleep_time                         <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_wake_time                          <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ n_waso                               <dbl> 7, 7, 7, NA, 6, 12, NA, 7, 8, 6, 5, 7, 1, 7, 7, 7, 7, 6,...
$ `Std Dev(n-1)_avg_sleep_b`           <dbl> 3.15, 8.00, 3.40, NA, 10.54, 8.53, NA, 15.04, 3.73, 7.52...
$ `Std Dev(n-1)_avg_wake_b`            <dbl> 1.45, 0.64, 1.50, NA, 0.41, 1.68, NA, 1.73, 1.33, 0.24, ...
$ `Std Dev(n-1)_duration`              <dbl> 57.51, 36.82, 98.69, NA, 39.15, 51.15, NA, 57.07, 54.48,...
$ `Std Dev(n-1)_efficiency`            <dbl> 7.44, 7.44, 11.66, NA, 1.81, 7.97, NA, 17.18, 6.36, 4.87...
$ `Std Dev(n-1)_number_of_sleep_bouts` <dbl> 4.89, 5.88, 3.45, NA, 3.72, 3.12, NA, 6.07, 6.53, 3.27, ...
$ `Std Dev(n-1)_number_of_wake_bouts`  <dbl> 4.78, 6.18, 3.55, NA, 3.87, 2.95, NA, 6.00, 6.35, 3.73, ...
$ `Std Dev(n-1)_onset_latency`         <dbl> 22.09, 21.25, 58.46, NA, 4.59, 16.31, NA, 55.31, 23.12, ...
$ `Std Dev(n-1)_percent_invalid_sw`    <dbl> 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, NaN, 0, 0, 0, 0, 0...
$ `Std Dev(n-1)_percent_sleep`         <dbl> 8.10, 5.41, 3.69, NA, 1.08, 6.03, NA, 12.98, 4.45, 1.49,...
$ `Std Dev(n-1)_percent_wake`          <dbl> 8.10, 5.41, 3.69, NA, 1.08, 6.03, NA, 12.98, 4.45, 1.49,...
$ `Std Dev(n-1)_sleep_time`            <dbl> 26.50, 48.24, 74.86, NA, 36.42, 49.63, NA, 88.66, 54.85,...
$ `Std Dev(n-1)_wake_time`             <dbl> 44.26, 27.11, 27.33, NA, 6.00, 31.06, NA, 50.30, 21.22, ...
$ `Std Dev(n-1)_waso`                  <dbl> 44.26, 27.11, 27.33, NA, 6.00, 31.06, NA, 50.30, 21.22, ...

glimpse(data_lf)

Observations: 334
Variables: 94
$ ID                                   <dbl> 11002, 11002, 11002, 11002, 11002, 11004, 11004, 11004, ...
$ Episode                              <dbl> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4,...
$ sens_fin                             <dbl> 4.0, 3.5, 2.5, 2.0, 3.5, 4.5, 5.0, 3.0, 3.5, 3.0, 3.5, 3...
$ intrus_fin                           <dbl> 3.5, 5.0, 6.0, 6.5, 4.5, 2.0, 2.5, 6.0, 5.5, 6.0, 5.0, 5...
$ posreg_fin                           <dbl> 5.5, 6.0, 5.0, 3.5, 5.0, 6.0, 6.0, 6.0, 5.5, 5.0, 5.0, 4...
$ stim_fin                             <dbl> 3.5, 4.0, 3.5, 3.0, 3.5, 3.0, 4.5, 2.0, 3.0, 2.0, 3.0, 2...
$ detach_fin                           <dbl> 1.0, 1.0, 1.0, 1.5, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0, 1.0, 1...
$ negreg_fin                           <dbl> 1.0, 1.0, 1.5, 2.0, 1.0, 1.0, 1.0, 1.0, 1.5, 1.5, 1.5, 2...
$ Condition                            <chr> "ABTI", "ABTI", "ABTI", "ABTI", "ABTI", "CBTI", "CBTI", ...
$ ISI_total                            <int> 15, 15, 15, 15, 15, 9, 9, 9, 9, 9, 11, 11, 11, 11, 11, 1...
$ ISIP_total                           <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
$ EPDS_total                           <int> 12, 12, 12, 12, 12, 4, 4, 4, 4, 4, 12, 12, 12, 12, 12, 4...
$ B_sleepnight                         <dbl> NA, NA, NA, NA, NA, 600, 600, 600, 600, 600, 600, 600, 6...
$ B_sleepday                           <dbl> NA, NA, NA, NA, NA, 180, 180, 180, 180, 180, 270, 270, 2...
$ B_nightwakeful                       <dbl> NA, NA, NA, NA, NA, 90, 90, 90, 90, 90, 10, 10, 10, 10, ...
$ B_gotosleep                          <dbl> NA, NA, NA, NA, NA, 120, 120, 120, 120, 120, 0, 0, 0, 0,...
$ analysis_name                        <chr> "BS_6.16.17", "BS_6.16.17", "BS_6.16.17", "BS_6.16.17", ...
$ data_start_date                      <chr> "11/12/13", "11/12/13", "11/12/13", "11/12/13", "11/12/1...
$ data_start_time                      <time> 11:15:00, 11:15:00, 11:15:00, 11:15:00, 11:15:00, 14:09...
$ interval_type                        <chr> "Sleep Summary", "Sleep Summary", "Sleep Summary", "Slee...
$ start_date                           <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, N...
$ start_time                           <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, N...
$ end_date                             <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, N...
$ end_time                             <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, N...
$ `Average(n)_avg_sleep_b`             <dbl> 10.75, 10.75, 10.75, 10.75, 10.75, 24.35, 24.35, 24.35, ...
$ `Average(n)_avg_wake_b`              <dbl> 3.92, 3.92, 3.92, 3.92, 3.92, 2.35, 2.35, 2.35, 2.35, 2....
$ `Average(n)_duration`                <dbl> 361.29, 361.29, 361.29, 361.29, 361.29, 512.57, 512.57, ...
$ mean_efficiency                      <dbl> 63.08, 63.08, 63.08, 63.08, 63.08, 84.85, 84.85, 84.85, ...
$ `Average(n)_number_of_sleep_bouts`   <dbl> 25.43, 25.43, 25.43, 25.43, 25.43, 20.57, 20.57, 20.57, ...
$ `Average(n)_number_of_wake_bouts`    <dbl> 25.14, 25.14, 25.14, 25.14, 25.14, 20.29, 20.29, 20.29, ...
$ `Average(n)_onset_latency`           <dbl> 15.86, 15.86, 15.86, 15.86, 15.86, 23.43, 23.43, 23.43, ...
$ `Average(n)_percent_invalid_sw`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, NA,...
$ `Average(n)_percent_sleep`           <dbl> 73.21, 73.21, 73.21, 73.21, 73.21, 90.20, 90.20, 90.20, ...
$ `Average(n)_percent_wake`            <dbl> 26.79, 26.79, 26.79, 26.79, 26.79, 9.80, 9.80, 9.80, 9.8...
$ mean_sleep_time                      <dbl> 261.57, 261.57, 261.57, 261.57, 261.57, 462.71, 462.71, ...
$ mean_wake_time                       <dbl> 99.71, 99.71, 99.71, 99.71, 99.71, 49.86, 49.86, 49.86, ...
$ `Average(n)_waso`                    <dbl> 99.71, 99.71, 99.71, 99.71, 99.71, 49.86, 49.86, 49.86, ...
$ `Maximum(n)_avg_sleep_b`             <dbl> 17.41, 17.41, 17.41, 17.41, 17.41, 36.36, 36.36, 36.36, ...
$ `Maximum(n)_avg_wake_b`              <dbl> 6.44, 6.44, 6.44, 6.44, 6.44, 3.46, 3.46, 3.46, 3.46, 3....
$ `Maximum(n)_duration`                <dbl> 474, 474, 474, 474, 474, 559, 559, 559, 559, 559, 582, 5...
$ `Maximum(n)_efficiency`              <dbl> 72.54, 72.54, 72.54, 72.54, 72.54, 93.76, 93.76, 93.76, ...
$ `Maximum(n)_number_of_sleep_bouts`   <dbl> 31, 31, 31, 31, 31, 28, 28, 28, 28, 28, 20, 20, 20, 20, ...
$ `Maximum(n)_number_of_wake_bouts`    <dbl> 31, 31, 31, 31, 31, 28, 28, 28, 28, 28, 21, 21, 21, 21, ...
$ `Maximum(n)_onset_latency`           <dbl> 62, 62, 62, 62, 62, 56, 56, 56, 56, 56, 167, 167, 167, 1...
$ `Maximum(n)_percent_invalid_sw`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, NA,...
$ `Maximum(n)_percent_sleep`           <dbl> 83.62, 83.62, 83.62, 83.62, 83.62, 95.20, 95.20, 95.20, ...
$ `Maximum(n)_percent_wake`            <dbl> 36.98, 36.98, 36.98, 36.98, 36.98, 19.48, 19.48, 19.48, ...
$ `Maximum(n)_sleep_time`              <dbl> 300, 300, 300, 300, 300, 524, 524, 524, 524, 524, 479, 4...
$ `Maximum(n)_wake_time`               <dbl> 174, 174, 174, 174, 174, 97, 97, 97, 97, 97, 103, 103, 1...
$ `Maximum(n)_waso`                    <dbl> 174, 174, 174, 174, 174, 97, 97, 97, 97, 97, 103, 103, 1...
$ `Minimum(n)_avg_sleep_b`             <dbl> 7.84, 7.84, 7.84, 7.84, 7.84, 14.32, 14.32, 14.32, 14.32...
$ `Minimum(n)_avg_wake_b`              <dbl> 2.56, 2.56, 2.56, 2.56, 2.56, 1.67, 1.67, 1.67, 1.67, 1....
$ `Minimum(n)_duration`                <dbl> 297, 297, 297, 297, 297, 449, 449, 449, 449, 449, 279, 2...
$ `Minimum(n)_efficiency`              <dbl> 50.42, 50.42, 50.42, 50.42, 50.42, 72.12, 72.12, 72.12, ...
$ `Minimum(n)_number_of_sleep_bouts`   <dbl> 17, 17, 17, 17, 17, 14, 14, 14, 14, 14, 11, 11, 11, 11, ...
$ `Minimum(n)_number_of_wake_bouts`    <dbl> 17, 17, 17, 17, 17, 14, 14, 14, 14, 14, 11, 11, 11, 11, ...
$ `Minimum(n)_onset_latency`           <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, NA, NA, NA,...
$ `Minimum(n)_percent_invalid_sw`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, NA,...
$ `Minimum(n)_percent_sleep`           <dbl> 63.02, 63.02, 63.02, 63.02, 63.02, 80.52, 80.52, 80.52, ...
$ `Minimum(n)_percent_wake`            <dbl> 16.38, 16.38, 16.38, 16.38, 16.38, 4.80, 4.80, 4.80, 4.8...
$ `Minimum(n)_sleep_time`              <dbl> 235, 235, 235, 235, 235, 401, 401, 401, 401, 401, 240, 2...
$ `Minimum(n)_wake_time`               <dbl> 58, 58, 58, 58, 58, 25, 25, 25, 25, 25, 30, 30, 30, 30, ...
$ `Minimum(n)_waso`                    <dbl> 58, 58, 58, 58, 58, 25, 25, 25, 25, 25, 30, 30, 30, 30, ...
$ n_avg_sleep_b                        <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_avg_wake_b                         <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_duration                           <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_efficiency                         <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_number_of_sleep_bouts              <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_number_of_wake_bouts               <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_onset_latency                      <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_percent_invalid_sw                 <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_percent_sleep                      <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_percent_wake                       <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_sleep_time                         <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_wake_time                          <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ n_waso                               <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, NA, NA, NA,...
$ `Std Dev(n-1)_avg_sleep_b`           <dbl> 3.15, 3.15, 3.15, 3.15, 3.15, 8.00, 8.00, 8.00, 8.00, 8....
$ `Std Dev(n-1)_avg_wake_b`            <dbl> 1.45, 1.45, 1.45, 1.45, 1.45, 0.64, 0.64, 0.64, 0.64, 0....
$ `Std Dev(n-1)_duration`              <dbl> 57.51, 57.51, 57.51, 57.51, 57.51, 36.82, 36.82, 36.82, ...
$ `Std Dev(n-1)_efficiency`            <dbl> 7.44, 7.44, 7.44, 7.44, 7.44, 7.44, 7.44, 7.44, 7.44, 7....
$ `Std Dev(n-1)_number_of_sleep_bouts` <dbl> 4.89, 4.89, 4.89, 4.89, 4.89, 5.88, 5.88, 5.88, 5.88, 5....
$ `Std Dev(n-1)_number_of_wake_bouts`  <dbl> 4.78, 4.78, 4.78, 4.78, 4.78, 6.18, 6.18, 6.18, 6.18, 6....
$ `Std Dev(n-1)_onset_latency`         <dbl> 22.09, 22.09, 22.09, 22.09, 22.09, 21.25, 21.25, 21.25, ...
$ `Std Dev(n-1)_percent_invalid_sw`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, NA,...
$ `Std Dev(n-1)_percent_sleep`         <dbl> 8.10, 8.10, 8.10, 8.10, 8.10, 5.41, 5.41, 5.41, 5.41, 5....
$ `Std Dev(n-1)_percent_wake`          <dbl> 8.10, 8.10, 8.10, 8.10, 8.10, 5.41, 5.41, 5.41, 5.41, 5....
$ `Std Dev(n-1)_sleep_time`            <dbl> 26.50, 26.50, 26.50, 26.50, 26.50, 48.24, 48.24, 48.24, ...
$ `Std Dev(n-1)_wake_time`             <dbl> 44.26, 44.26, 44.26, 44.26, 44.26, 27.11, 27.11, 27.11, ...
$ `Std Dev(n-1)_waso`                  <dbl> 44.26, 44.26, 44.26, 44.26, 44.26, 27.11, 27.11, 27.11, ...
$ episode_re                           <int> 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3,...
$ wake_time_grp                        <chr> "higher", "higher", "higher", "higher", "higher", "lower...
$ efficiency_grp                       <chr> "lower", "lower", "lower", "lower", "lower", "higher", "...
$ ISI_grp                              <chr> "higher", "higher", "higher", "higher", "higher", "highe...
$ ISIP_grp                             <chr> "higher", "higher", "higher", "higher", "higher", "highe...

corr_data_wf <- data_wf %>%
  select(
    ISI_total,
    ISIP_total,
    EPDS_total,
    mean_wake_time,
    mean_sleep_time,
    mean_efficiency,
    B_nightwakeful,
    B_sleepnight,
    B_gotosleep,
    B_sleepday,
    Factor1,
    Factor2,
    Factor3,
    sens_M,
    intrus_M
  )
corr_data_wf <- cor(corr_data_wf, use = "pairwise.complete.obs")

Actigraphy measure of maternal sleep time is not associated with either version of the ISI; however, measure of time awake is positively associated with both of these measures.

Trajectories of caregiving across the Free Play

Plot trajectories

sensitivity

plot_trajectories_id(data_lf, episode_re, sens_fin)

##treatment arm
plot_trajectories_id(data_lf, episode_re, sens_fin) +
  facet_grid(~Condition)

plot_trajectories(data_lf, episode_re, sens_fin, Condition, method = "loess")

##wake time
plot_trajectories(
  data_lf %>% 
    filter(!is.na(wake_time_grp)),
  episode_re, 
  sens_fin, 
  wake_time_grp, 
  method = "loess"
) +
  theme(
    aspect.ratio = 1
  )

##efficiency
plot_trajectories(
  data_lf %>% 
    filter(!is.na(efficiency_grp)),
  episode_re, 
  sens_fin, 
  efficiency_grp, 
  method = "loess"
) +
  theme(
    aspect.ratio = 1
  )

intrusiveness

plot_trajectories_id(data_lf, episode_re, intrus_fin)

##treatment arm
plot_trajectories_id(data_lf, episode_re, intrus_fin) +
  facet_grid(~Condition)

plot_trajectories(data_lf, episode_re, intrus_fin, Condition, method = "loess")

##wake time
plot_trajectories(
  data_lf %>% 
    filter(!is.na(wake_time_grp)),
  episode_re, 
  intrus_fin, 
  wake_time_grp, 
  method = "loess"
) +
  theme(
    aspect.ratio = 1
  )

##efficiency
plot_trajectories(
  data_lf %>% 
    filter(!is.na(efficiency_grp)),
  episode_re, 
  intrus_fin, 
  efficiency_grp, 
  method = "loess"
) +
  theme(
    aspect.ratio = 1
  )

stimulation

plot_trajectories_id(data_lf, episode_re, stim_fin)

##treatment arm
plot_trajectories_id(data_lf, episode_re, stim_fin) +
  facet_grid(~Condition)

plot_trajectories(data_lf, episode_re, stim_fin, Condition, method = "loess")

##wake time
plot_trajectories(
  data_lf %>% 
    filter(!is.na(wake_time_grp)),
  episode_re, 
  stim_fin, 
  wake_time_grp, 
  method = "loess"
) +
  theme(
    aspect.ratio = 1
  )

##efficiency
plot_trajectories(
  data_lf %>% 
    filter(!is.na(efficiency_grp)),
  episode_re, 
  stim_fin, 
  efficiency_grp, 
  method = "loess"
) +
  theme(
    aspect.ratio = 1
  )

##insomnia severity
plot_trajectories(
  data_lf %>% 
    filter(!is.na(ISI_grp)),
  episode_re, 
  sens_fin, 
  ISI_grp, 
  method = "loess"
) +
  theme(
    aspect.ratio = 1
  )

##postpartum insomnia severity
plot_trajectories(
  data_lf %>% 
    filter(!is.na(ISIP_grp)),
  episode_re, 
  sens_fin, 
  ISIP_grp, 
  method = "loess"
) +
  theme(
    aspect.ratio = 1
  )

Base Sensitivity MLM models

sens_ml_1 <- lmer(sens_fin ~ Episode + (1|ID), REML = TRUE, data = data_lf)
summary(sens_ml_1)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: sens_fin ~ Episode + (1 | ID)
   Data: data_lf

REML criterion at convergence: 751.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.9126 -0.5782 -0.0674  0.6073  3.3244 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 0.7735   0.8795  
 Residual             0.3340   0.5779  
Number of obs: 331, groups:  ID, 67

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)   3.94409    0.13072 118.41920  30.171  < 2e-16 ***
Episode      -0.07689    0.02266 263.33712  -3.393 0.000798 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
        (Intr)
Episode -0.515

sens_ml_2 <- lmer(sens_fin ~ Episode + (Episode|ID), REML = TRUE, data = data_lf)
summary(sens_ml_2)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: sens_fin ~ Episode + (Episode | ID)
   Data: data_lf

REML criterion at convergence: 727.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.1995 -0.5539 -0.0203  0.5598  2.4362 

Random effects:
 Groups   Name        Variance Std.Dev. Corr 
 ID       (Intercept) 0.76140  0.8726        
          Episode     0.03381  0.1839   -0.28
 Residual             0.25163  0.5016        
Number of obs: 331, groups:  ID, 67

Fixed effects:
            Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)  3.94757    0.12474 66.23653  31.647   <2e-16 ***
Episode     -0.07863    0.02994 65.82245  -2.627   0.0107 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
        (Intr)
Episode -0.491

ICC_intercept = .7612 / (.7612 + .3327)
ICC_intercept

[1] 0.6958589

ICC_slope = .03292 / (.74591 + .25163 + .03292)
ICC_slope

[1] 0.0319469

Slope ICC is very low. Will not include in models.

Sensitivity ~ Wake time MLM

sens_ml_3 <- lmer(sens_fin ~ episode_re + mean_wake_time + (1|ID), REML = TRUE, data = data_lf)
summary(sens_ml_3)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: sens_fin ~ episode_re + mean_wake_time + (1 | ID)
   Data: data_lf

REML criterion at convergence: 653.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.8461 -0.5808 -0.0710  0.6238  3.2206 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 0.6947   0.8335  
 Residual             0.3492   0.5909  
Number of obs: 282, groups:  ID, 57

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      4.387178   0.286909  58.330525  15.291  < 2e-16 ***
episode_re      -0.077549   0.025075 224.280432  -3.093  0.00224 ** 
mean_wake_time  -0.008019   0.004507  54.971327  -1.779  0.08073 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) epsd_r
episode_re  -0.169       
mean_wak_tm -0.898 -0.003

sens_ml_4 <- lmer(sens_fin ~ episode_re * mean_wake_time + (1|ID), REML = TRUE, data = data_lf)
sens_ml_5 <- lmer(sens_fin ~ episode_re * scale(mean_wake_time, scale = FALSE) + (1|ID), REML = TRUE, data = data_lf)
summary(sens_ml_5)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: sens_fin ~ episode_re * scale(mean_wake_time, scale = FALSE) +      (1 | ID)
   Data: data_lf

REML criterion at convergence: 660.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.8475 -0.6000 -0.0284  0.6336  3.3263 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 0.6951   0.8337  
 Residual             0.3436   0.5861  
Number of obs: 282, groups:  ID, 57

Fixed effects:
                                                  Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                                      3.927e+00  1.258e-01  7.598e+01  31.216  < 2e-16 ***
episode_re                                      -7.687e-02  2.488e-02  2.233e+02  -3.090  0.00225 ** 
scale(mean_wake_time, scale = FALSE)            -3.874e-03  4.893e-03  7.591e+01  -0.792  0.43100    
episode_re:scale(mean_wake_time, scale = FALSE) -2.087e-03  9.619e-04  2.232e+02  -2.170  0.03108 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) epsd_r s(_s=F
episode_re  -0.390              
s(__,s=FALS  0.003  0.002       
e_:(__,s=FA  0.002 -0.013 -0.390

johnson_neyman(model = sens_ml_4, pred = episode_re, modx = mean_wake_time)

JOHNSON-NEYMAN INTERVAL 

When mean_wake_time is OUTSIDE the interval [-351.16, 46.14], the slope of episode_re is p
< .05.

Note: The range of observed values of mean_wake_time is [17.67, 138.50]

Intrusiveness ~ Wake time MLM

intrus_ml_1 <- lmer(intrus_fin ~ episode_re + mean_wake_time + (1|ID), REML = TRUE, data = data_lf)
summary(intrus_ml_1)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: intrus_fin ~ episode_re + mean_wake_time + (1 | ID)
   Data: data_lf

REML criterion at convergence: 873.6

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.67203 -0.57194 -0.01982  0.59396  2.62547 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 1.2915   1.1365  
 Residual             0.7975   0.8931  
Number of obs: 282, groups:  ID, 57

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)    2.483e+00  3.965e-01 5.899e+01   6.263 4.71e-08 ***
episode_re     1.297e-01  3.789e-02 2.243e+02   3.423 0.000736 ***
mean_wake_time 4.956e-03  6.210e-03 5.494e+01   0.798 0.428229    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) epsd_r
episode_re  -0.185       
mean_wak_tm -0.896 -0.004

sens_ml_5 <- lmer(intrus_fin ~ episode_re * scale(mean_wake_time, scale = FALSE) + (1|ID), REML = TRUE, data = data_lf)
summary(sens_ml_5)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: intrus_fin ~ episode_re * scale(mean_wake_time, scale = FALSE) +      (1 | ID)
   Data: data_lf

REML criterion at convergence: 882.2

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.70380 -0.57587  0.01841  0.58062  2.60660 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 1.2931   1.1372  
 Residual             0.7915   0.8897  
Number of obs: 282, groups:  ID, 57

Fixed effects:
                                                 Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)                                     2.768e+00  1.762e-01 8.065e+01  15.706  < 2e-16 ***
episode_re                                      1.289e-01  3.776e-02 2.233e+02   3.415 0.000757 ***
scale(mean_wake_time, scale = FALSE)            2.047e-04  6.854e-03 8.057e+01   0.030 0.976247    
episode_re:scale(mean_wake_time, scale = FALSE) 2.392e-03  1.460e-03 2.232e+02   1.639 0.102692    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) epsd_r s(_s=F
episode_re  -0.423              
s(__,s=FALS  0.003  0.002       
e_:(__,s=FA  0.002 -0.013 -0.423

Intrusiveness ~ Wake time MLM

stim_ml_1 <- lmer(stim_fin ~ episode_re + mean_wake_time + (1|ID), REML = TRUE, data = data_lf)
summary(stim_ml_1)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: stim_fin ~ episode_re + mean_wake_time + (1 | ID)
   Data: data_lf

REML criterion at convergence: 548.1

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.42828 -0.56784 -0.03077  0.53039  2.88986 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 0.5538   0.7442  
 Residual             0.2769   0.5263  
Number of obs: 262, groups:  ID, 53

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      3.330566   0.264805  54.052543  12.577  < 2e-16 ***
episode_re      -0.065119   0.023183 208.244551  -2.809  0.00544 ** 
mean_wake_time  -0.002921   0.004128  50.939234  -0.708  0.48245    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) epsd_r
episode_re  -0.169       
mean_wak_tm -0.898 -0.004

stim_ml_5 <- lmer(stim_fin ~ episode_re * scale(mean_wake_time, scale = FALSE) + (1|ID), REML = TRUE, data = data_lf)
summary(stim_ml_5)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: stim_fin ~ episode_re * scale(mean_wake_time, scale = FALSE) +      (1 | ID)
   Data: data_lf

REML criterion at convergence: 559.5

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.32778 -0.54639 -0.02948  0.55324  2.90093 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 0.5531   0.7437  
 Residual             0.2772   0.5265  
Number of obs: 262, groups:  ID, 53

Fixed effects:
                                                  Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                                      3.162e+00  1.166e-01  7.068e+01  27.125  < 2e-16 ***
episode_re                                      -6.449e-02  2.320e-02  2.073e+02  -2.779  0.00595 ** 
scale(mean_wake_time, scale = FALSE)            -1.306e-03  4.486e-03  7.061e+01  -0.291  0.77190    
episode_re:scale(mean_wake_time, scale = FALSE) -8.140e-04  8.872e-04  2.071e+02  -0.918  0.35993    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) epsd_r s(_s=F
episode_re  -0.392              
s(__,s=FALS -0.013  0.008       
e_:(__,s=FA  0.008 -0.029 -0.393

ISI MLM

sens_ml_6 <- lmer(sens_fin ~ episode_re + ISI_total + (1|ID), REML = TRUE, data = data_lf)
summary(sens_ml_6)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: sens_fin ~ episode_re + ISI_total + (1 | ID)
   Data: data_lf

REML criterion at convergence: 748.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.9180 -0.6178 -0.0584  0.6324  3.2871 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 0.7767   0.8813  
 Residual             0.3381   0.5815  
Number of obs: 326, groups:  ID, 66

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)   4.05916    0.19899  71.04873  20.399  < 2e-16 ***
episode_re   -0.07721    0.02298 259.31256  -3.360 0.000896 ***
ISI_total    -0.02560    0.02196  64.00907  -1.166 0.247975    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
           (Intr) epsd_r
episode_re -0.225       
ISI_total  -0.790 -0.003

sens_ml_7 <- lmer(sens_fin ~ episode_re * scale(ISI_total, scale = FALSE) + (1|ID), REML = TRUE, data = data_lf)
summary(sens_ml_7)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: sens_fin ~ episode_re * scale(ISI_total, scale = FALSE) + (1 |      ID)
   Data: data_lf

REML criterion at convergence: 757.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.8780 -0.6175 -0.0626  0.6313  3.3048 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 0.7763   0.8811  
 Residual             0.3393   0.5825  
Number of obs: 326, groups:  ID, 66

Fixed effects:
                                             Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                                  3.875127   0.121908  85.686234  31.787  < 2e-16 ***
episode_re                                  -0.077093   0.023019 258.317516  -3.349 0.000932 ***
scale(ISI_total, scale = FALSE)             -0.022420   0.023652  85.671387  -0.948 0.345834    
episode_re:scale(ISI_total, scale = FALSE)  -0.001612   0.004460 258.293306  -0.361 0.718052    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) epsd_r s(Is=F
episode_re  -0.372              
s(ISI_,s=FA  0.003  0.002       
e_:(ISI_s=F  0.002 -0.014 -0.372

ISI MLM

sens_ml_8 <- lmer(sens_fin ~ episode_re + ISIP_total + (1|ID), REML = TRUE, data = data_lf)
summary(sens_ml_8)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: sens_fin ~ episode_re + ISIP_total + (1 | ID)
   Data: data_lf

REML criterion at convergence: 627

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.8979 -0.6069 -0.0724  0.6142  3.3858 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 0.8690   0.9322  
 Residual             0.3323   0.5764  
Number of obs: 272, groups:  ID, 55

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)   4.10438    0.19275  60.42897  21.294   <2e-16 ***
episode_re   -0.05688    0.02492 216.17119  -2.283   0.0234 *  
ISIP_total   -0.05215    0.03078  52.96140  -1.694   0.0961 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
           (Intr) epsd_r
episode_re -0.252       
ISIP_total -0.690 -0.004

sens_ml_9 <- lmer(sens_fin ~ episode_re * scale(ISIP_total, scale = FALSE) + (1|ID), REML = TRUE, data = data_lf)
summary(sens_ml_9)

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: sens_fin ~ episode_re * scale(ISIP_total, scale = FALSE) + (1 |      ID)
   Data: data_lf

REML criterion at convergence: 634.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.8244 -0.5975 -0.0645  0.6143  3.4208 

Random effects:
 Groups   Name        Variance Std.Dev.
 ID       (Intercept) 0.8681   0.9317  
 Residual             0.3329   0.5770  
Number of obs: 272, groups:  ID, 55

Fixed effects:
                                              Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                                   3.877553   0.139400  68.799482  27.816   <2e-16 ***
episode_re                                   -0.056487   0.024946 215.177942  -2.264   0.0245 *  
scale(ISIP_total, scale = FALSE)             -0.043065   0.032885  68.776914  -1.310   0.1947    
episode_re:scale(ISIP_total, scale = FALSE)  -0.004594   0.005869 215.139917  -0.783   0.4346    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) epsd_r s(Is=F
episode_re  -0.353              
s(ISIP_,s=F  0.003  0.003       
e_:(ISIPs=F  0.003 -0.020 -0.353

Means across intervention arms

data_wf %>% 
  group_by(Condition) %>%
  summarise_at(
    vars(
      ISI_total, 
      ISIP_total, 
      mean_wake_time,
      mean_efficiency,
      mean_sleep_time,
      sens_M:negreg_M
    ),
    funs(mean),
    na.rm = TRUE
  )

No substantial differences across actigraphy, self-report, or observational measures between though who received CBTI vs. ABTI.

Latent Class Mixture Model

library(lcmm)
lcmm_sens_df <-
  data_lf %>% 
  select(
    ID,
    episode_re,
    sens_fin
  ) %>% 
  mutate(
    ID = as.character(ID)
  )
lcmm_sens <-
  lcmm(sens_fin~episode_re, random = ~episode_re, subject = "ID", mixture = ~episode_re, ng = 2, idiag = TRUE, data = lcmm_sens_df, link = "linear")

Be patient, lcmm is running ... 
The program took 0.07 seconds 

summary(lcmm_sens)

General latent class mixed model 
     fitted by maximum likelihood method 
 
lcmm(fixed = sens_fin ~ episode_re, mixture = ~episode_re, random = ~episode_re, 
    subject = "ID", ng = 2, idiag = TRUE, link = "linear", data = lcmm_sens_df)
 
Statistical Model: 
     Dataset: lcmm_sens_df 
     Number of subjects: 67 
     Number of observations: 331 
     Number of observations deleted: 3 
     Number of latent classes: 2 
     Number of parameters: 8  
     Link function: linear  
 
Iteration process: 
     Convergence criteria satisfied 
     Number of iterations:  13 
     Convergence criteria: parameters= 9.2e-07 
                         : likelihood= 9.3e-07 
                         : second derivatives= 4.3e-12 
 
Goodness-of-fit statistics: 
     maximum log-likelihood: -358.96  
     AIC: 733.92  
     BIC: 751.55  
 
 
Maximum Likelihood Estimates: 
 
Fixed effects in the class-membership model:
(the class of reference is the last class) 

                     coef      Se   Wald p-value
intercept class1 -0.68692 0.72539 -0.947 0.34365

Fixed effects in the longitudinal model:

                                     coef      Se   Wald p-value
intercept class1 (not estimated)        0                       
intercept class2                 -2.67637 0.53010 -5.049 0.00000
episode_re class1                -0.22021 0.16321 -1.349 0.17726
episode_re class2                -0.12519 0.10202 -1.227 0.21976


Variance-covariance matrix of the random-effects:
           intercept episode_re
intercept       1.19           
episode_re      0.00    0.13253

Residual standard error (not estimated) = 1

Parameters of the link function:

                         coef      Se   Wald p-value
Linear 1 (intercept)  4.76027 0.27378 17.387 0.00000
Linear 2 (std err)    0.50062 0.02592 19.317 0.00000

postprob(lcmm_sens)

 
Posterior classification: 
  class1 class2
N  22.00  45.00
%  32.84  67.16
 
Posterior classification table: 
     --> mean of posterior probabilities in each class 
        prob1  prob2
class1 0.7887 0.2113
class2 0.1128 0.8872
 
Posterior probabilities above a threshold (%): 
         class1 class2
prob>0.7  59.09  84.44
prob>0.8  45.45  77.78
prob>0.9  36.36  64.44
 

NOTES: DUKE awakenings (amount of time awake) from mom and due to infant early childhood adversity look at variability in sensitivity